Biochem. J. (2013) 449, 427–435 (Printed in Great Britain)
427
doi:10.1042/BJ20120980
Inferring the metabolism of human orphan metabolites from their metabolic
network context affirms human gluconokinase activity
Óttar ROLFSSON*†, Giuseppe PAGLIA*, Manuela MAGNUSDÓTTIR*, Bernhard Ø. PALSSON* and Ines THIELE*‡1
*Center for Systems Biology, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland, †University of Iceland Biomedical Center, Laeknagardur, 101 Reykjavik, Iceland, and
‡Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland,101 Reykjavik, Iceland
Metabolic network reconstructions define metabolic information
within a target organism and can therefore be used to address
incomplete metabolic information. In the present study we
used a computational approach to identify human metabolites
whose metabolism is incomplete on the basis of their detection
in humans but exclusion from the human metabolic network
reconstruction RECON 1. Candidate solutions, composed of
metabolic reactions capable of explaining the metabolism of these
compounds, were then identified computationally from a global
biochemical reaction database. Solutions were characterized with
respect to how metabolites were incorporated into RECON 1
and their biological relevance. Through detailed case studies
we show that biologically plausible non-intuitive hypotheses
regarding the metabolism of these compounds can be proposed
in a semi-automated manner, in an approach that is similar to de
novo network reconstruction. We subsequently experimentally
validated one of the proposed hypotheses and report that
C9orf103, previously identified as a candidate tumour suppressor
gene, encodes a functional human gluconokinase. The results of
the present study demonstrate how semi-automatic gap filling can
be used to refine and extend metabolic reconstructions, thereby
increasing their biological scope. Furthermore, we illustrate how
incomplete human metabolic knowledge can be coupled with gene
annotation in order to prioritize and confirm gene functions.
INTRODUCTION
knowledge gaps and, subsequently, suggesting candidate gapfilling reactions is referred to as network gap filling [10–12]. Because BiGG reconstructions can be converted into a mathematical
format, gap filling is carried out computationally. The SMILEY
gap-filling algorithm, for example, has been successfully used
to unravel novel metabolic functions in Escherichia coli [13]
and to propose novel metabolic functions in humans [8]. Similar
metabolic pathway discovery algorithms have found widespread
use in biotechnology for pathway discovery [14].
The global human genome-scale metabolic reconstruction
RECON 1 represents one of the most comprehensive BiGG
metabolic reconstructions generated to date [15]. RECON 1 has
found diverse applications in human metabolic research (reviewed
in [16]) and has the potential to facilitate novel metabolic
reaction discovery through a gap-filling approach. Although
successful examples exist for bacterial metabolism [9,17], the use
of eukaryotic metabolic models to this end has received limited
attention [8].
Although comprehensive, RECON 1, and indeed all genomescale reconstructions, is incomplete as knowledge of metabolism
accumulates over time. Incorporation of acquired biological
data can be performed using a gap-filling approach. If the
incorporated biological components are incompletely annotated,
then hypotheses of their biological functions can be generated
from the biological network context. In the present study, we
set out to identify and characterize human orphan metabolites
that were defined here as metabolites that have been detected in
human biofluids but were not accounted for in RECON 1. The
present study aimed to elucidate the metabolism of these orphan
metabolites in humans and simultaneously expand the biological
The wealth of genomic and metabolic information that has
amounted in the past decade has highlighted a need for methods
that facilitate the functional annotation of biochemicals that
take part in human metabolism [1]. Components of the human
metabolome can be categorized on the basis of their degree of
functional annotation into well-annotated, incompletely annotated
and unknown components. The first category provides the
basis for BiGG (biochemically, genetically and genomically)
structured metabolic networks that have found widespread use
in high-throughput data analysis, biochemical engineering and
pathway discovery [2,3]. The latter two categories reflect that
our knowledge of human metabolism is incomplete. Underlying
these are (i) genes and proteins of unknown function, (ii) proteins
that are not associated with a sequence (orphan enzymes) and
(iii) metabolites, for which metabolic knowledge is incomplete.
Estimates report that approximately 30–50 % of all enzymes
associated with EC (Enzyme Commission) numbers are
not associated with genes [4–6] and that the function of over
50 % of genes in higher organisms have not been experimentally
determined [7]. Dead-end metabolites, i.e. compounds whose
anabolic or catabolic route within humans are not completely
defined, are also numerous [8].
BiGG metabolic network reconstructions contain components
of an organism’s metabolome, from information on genes and
metabolic enzymes to metabolic reactants and products, and on
how these components interact [9]. Incomplete genetic, genomic
and biochemical information can be identified as network gaps
in BiGG reconstructions. The two-step procedure of identifying
Key words: gap filling, human metabolism, metabolic network,
RECON 1.
Abbreviations used: BiGG, biochemically, genetically and genomically; EC, Enzyme Commission; GFP, green fluorescent protein; HMDB, Human
Metabolome Database; IS, internal standard; LC-MS, liquid chromatography-MS; 6PGDH; 6-phosphogluconate dehydrogenase; PLP, pyrridoxal 5phosphate; PPP, pentose phosphate pathway.
1
To whom correspondence should be addressed (email [email protected]).
c The Authors Journal compilation c 2013 Biochemical Society
428
Ó. Rolfsson and others
accuracy of RECON 1. To our knowledge the systematic
identification and characterization of human orphan metabolites
has not been carried out previously. We used a gap-filling
algorithm to compute reaction hypotheses that could account for
these metabolites in humans and show that the approach not only
helps to reconstruct known biochemical pathways, as expected,
but is also capable of generating biologically plausible nonintuitive reaction hypotheses. We exemplified this by validating
one of our predictions experimentally, which resulted in the
characterization of a human gluconokinase.
EXPERIMENTAL
Pre-processing to metabolite integration
RECON 1 was downloaded from the BiGG database [18]. The
reconstruction was imported into Matlab (MathWorks) using the
COBRA toolbox [19]. Subsequently, we decompartmentalized all
reactions in RECON 1 by placing all intracellular compartment
reactions into the cytosol and removing duplicates. Extraorganism-located reactions were kept. We downloaded the KEGG
Ligand database (version as of 10/2009) [20] to obtain a universal
reference reaction list. For each metabolite present in RECON 1
and in the universal database, we created a transport reaction from
cytosol to the extra-organism space. Furthermore, we downloaded
the HMDB (Human Metabolome Database, release 2.5) [21]. We
mapped the entries from the HMDB on to RECON 1 using the
BiGG identifiers provided by the HMDB.
the basis of a literature review was made to select the most
biologically relevant solution. These criteria generated a filtered
candidate solution output (S1 and S2), which is reported in the
Results section (Supplementary Table S2 at http://www.biochemj.
org/bj/449/bj4490427add.htm).
Candidate reactions comprising the S1 and S2 solutions were
investigated individually to generate plausible hypotheses for the
gap filling of RECON 1. The orphan metabolites’ biological
origin was determined and the reactions composing the gap-filling
solutions were assessed on the basis of the underlying literature
and their annotations within multiple reaction and metabolite
databases. Biochemical and metabolic information was obtained
from the HMDB [21], the HSDB (Hazardous Substances
Database; http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?HSDB)
and the KEGG database [20]. For resolving reactions, not
annotated in humans, BLAST homology searches of genes
encoding the resolving reaction activity were performed against
the human sequence (http://blast.ncbi.nlm.nih.gov/Blast.cgi) and
using STRING [22]. On the basis of literature and electronic
data, solutions were classified as: (i) accepted, solutions were
composed of reactions for which genetic or biochemical data
had been reported in humans or closely related species;
(ii) hypothesis, solutions were biochemically plausible but
genetic or biochemical information was incomplete and requires
experimental investigation; (iii) discarded, solutions were
composed of reactions for which no evidence could be found
in humans or closely related species; and (iv) no solution,
the gap-filling algorithm failed to generate a solution. All
metabolic reaction and subcellular compartment abbreviations are
as described in the BiGG database [18].
Computation of candidate solutions using gap filling
The gap-filling algorithm has been described previously [9].
We used the implementation provided by the COBRA toolbox.
Briefly, RECON 1 served as the model to which reactions from
either the universal reaction list and/or the transport reaction
list should be added. In order to identify candidate anabolic
and catabolic solutions for each metabolite, we added a sink
(sink_A: Ø→1 A) or demand reaction (DM_A: 1 A→Ø) to the
model and performed the gap-filling analysis by requiring
the flux through either DM_A or sink_A to be greater than zero.
Up to ten solutions for each metabolite were computed. The
gap-filling algorithm requires metabolites in the reconstruction
to have KEGG identifiers in order to calculate metabolic
reaction solutions. This restrained the metabolites identified to
metabolites from the HMDB with KEGG identifiers. Note that
not all metabolites in RECON 1 have a KEGG identifier, which
may also limit the identification of candidate solutions from the
KEGG database.
Gap analysis and validation of the gap-filling solution output
The gap-filling algorithm generated a total of 1131 solutions for
the 343 orphan metabolites identified. In the majority of cases
metabolites either had five solutions or only one (Supplementary
Table S1 at http://www.biochemj.org/bj/449/bj4490427add.htm).
Solutions were filtered with the following criteria in mind: (i)
only solutions containing gene-associated resolving reactions
were picked; (ii) if no solution met this criteria, solutions with
the least number of resolving reactions and leading to integration
of the metabolite within the network as opposed to transport
were picked; and (iii) if no such solution was found, then
the solution involving the least number of resolving reactions
was picked, irrespective of the resolving reaction type. In cases
where two solutions met these criteria an informed decision on
c The Authors Journal compilation c 2013 Biochemical Society
Overexpression of C9orf103 and enzyme assays
C9orf103 isoform I (BI826543) and isoform II (BC142991)
were directionally cloned into a pT7CFE1-CHis expression
vector (Thermo Scientific) at the BamHI and XhoI restriction
sites following amplification using the forward primers 5 -GGAATTCCATATGGGATCCGCGGCGCCGGGCGCGCTGCTG3 and 5 -GGAATTCCATATGGGATCCAAGAAACCGAGGCCCGCGGCTGC-3 for isoforms I and II respectively, and the
reverse primer 5 -TATCTCGCTCGAGTTTCATTTTTAGGGTTTCCATAATTG-3 . This generated two vector constructs,
pT7CFE1-GlncKIsoI and pT7CFE1-GlnKIsoII, which, following
linearization with SpeI, were used in 25 μl coupled in vitro
transcription/translation reactions using the Pierce® Human
In Vitro Protein Expression kit for 6 h at 30 ◦ C. Confirmation of
overexpressed products was performed by Western blotting using
the HisProbe-HRP reagents and kit (Thermo Scientific). GFP
(green fluorescent protein) expressed from pCFE-GFP (Thermo
Scientific) was used as a positive control in in vitro translation
reactions and the gluconokinase assay. Following translation,
lysates were diluted in 1×kinase reaction buffer [100 mM
phosphate, 40 mM NaCl and 2.5 mM MgCl2 (pH 7.2)] to a
volume of 50 μl and desalted using spin columns with a 7.5 kDa
molecular mass cut-off. A 12 μl volume of this diluted desalted
lysate was then used in gluconokinase assays. The gluconokinase
assay was carried out in 33 μl reaction volumes in kinase reaction
buffer with 0.1 mM or 1 mM gluconate and 1 mM ATP. Purified
recombinant E. coli gluconokinase (50 ng; Megazyme, specific
activity 31.8 units/mg) was added and used as a positive
control. Following mixing by pipetting, reaction mixtures were
immediately split into 20 μl and 13 μl aliquots. The 20 μl aliquot
was incubated for 2 h at room temperature (20 ◦ C), and then
frozen at − 80 ◦ C for MS analysis. To the 13 μl aliquot, 2 μl
Gap-filling metabolic networks post-reconstruction
429
of 6PGDH (6-phosphogluconate dehydrogenase) master mix
was added and incubated at room temperature for 2 h. NADPH
production was measured by monitoring fluorescence at 445 nm
following excitation at 340 nm and interpolating values to that
of an NADPH standard curve. The 6PGDH master mix resulted
in the addition of 0.015 units of 6PGDH (Megazyme) and a
625 μM final concentration of NADP + . Following fluorescence
measurements, a 3 μl aliquot was removed from the kinase
reaction, diluted to 30 μl with kinase reaction buffer and the
ATP content of the reaction was determined with a CellTiter-Glo
assay (Promega) according to the manufacturer’s instructions.
All data are results of averaging over three replicate samples and
were similar in n = 3 repeats.
LC-MS (liquid chromatography-MS)
A UPLC system (UPLC Acquity, Waters) coupled in-line with a
quadrupole-time-of-flight hybrid mass spectrometer (Synapt G2,
Waters) was used. Separation was achieved by HILIC (hydrophilic
interaction chromatography) analysis on an 1.7 μm Acquity
amide column (2.1 mm×150 mm) (Waters). An electrospray
ionization interface was used in negative mode to direct column
eluent to the mass spectrometer. The flow rate was 0.4 ml/min.
Mobile phase A was 10 mM acetonitrile/sodium bicarbonate
(95:5) and mobile phase B contained 10 mM acetonitrile/sodium
bicarbonate (5:95). The following elution gradient was used:
0 min at 99 % A, 3 min at 20 % A, 4 min at 99 % A and 5 min at
99 % A. A capillary voltage of 1.8 kV and a cone voltage of 35 V
was used. MS spectra were acquired in centroid mode from m/z 50
to 1000 using a scan time of 0.35 s. Leucine enkephalin (2 ng/μl)
was used as the lock mass (m/z 554.2615). [13 C6 ]Gluconate was
used as the IS (internal standard) for enzyme assays when
gluconate was used as the substrate, whereas gluconate was
used as the IS for enzyme assays when [13 C6 ]gluconate was used
as the substrate. A 5 μl volume of IS (0.6 mM) was added to
20 μl of sample. Successively 300 μl of methanol was added
and samples were vortex-mixed for 5 min and centrifuged for
10 min at 10 000 g. Supernatants were recovered, dried under
nitrogen and reconstituted in 80 μl of acetonitrile/water (50:50,
v:v). Samples (5 μl) were injected into the UPLC-MS system.
RESULTS
Identification and characterization of human orphan metabolites
We aimed to identify human orphan metabolites. Therefore we
compared metabolites in RECON 1 with those that have been
detected in human biofluids as reported within the HMDB
database [21] (Figure 1A). A total of 343 metabolites were
identified whose metabolic fate was not captured in RECON 1
but can be assumed to have a metabolic degradation or production
route within humans on account of their detection in biofluids.
The metabolites were characterized on the basis of their
chemical taxonomy super-classes (Figure 2A). Organic acids,
both aromatic and linear, followed by various alcohols and
amino acid derivatives were found to be the most numerous.
These compound classes complement the gaps caused by blocked
reactions identified in RECON 1 reported previously, where
the largest proportion of blocked reactions have been observed
in amino acid metabolism, carbohydrate metabolism and lipid
metabolism [8]. The majority of the metabolites were detected
in either blood or urine and reflects that these biofluids are
most commonly investigated due to ease of sample access
(Figure 2B). Numerous metabolites were identified that would
Figure 1 Identification of human orphan metabolites and of reactions
capable of explaining their metabolic role
(A) Orphan metabolites were defined and identified as metabolites that have been detected in
human biofluids but not accounted for in RECON 1 (1). A modified network gap-filling algorithm
was used to propose the manner in which metabolites could be incorporated into RECON 1
using non-organism-specific metabolic reactions from the KEGG database or transport reactions
(2–3). Following validation of the biological relevance of proposed solutions (3), metabolites
could be incorporated into an updated metabolic reconstruction (4). (B) A toy network showing
the computed candidate solution types. Metabolite A is incorporated into the toy network with two
solution routes (S1 and S2), which couple the production and consumption of A to metabolites
already accounted for in the network (dark grey). A type II solution involves transport into the
cell and incorporation into the network as shown for metabolite B. A type III solution involves
transport into and out of the cell as shown for metabolite C. Note that solution routes S1 and/or
S2 can be composed of multiple reactions.
not normally be associated with basal metabolism, for example,
drug derivatives and plant-derived antioxidants. Some of these
metabolites, therefore, represent metabolic scope gaps [23], as
they lie outside the biological scope of RECON 1 and/or are
involved in non-metabolic pathways.
Incorporation of orphan metabolites into RECON 1
In order to incorporate the identified metabolites into RECON 1,
we computed candidate solutions, composed of non-organismspecific metabolic reactions from the KEGG database that could
account for the metabolic production and consumption of each
metabolite within RECON 1 (see the Experimental section).
Metabolites that could not be incorporated using reactions from
KEGG were connected to RECON 1 using an artificial transport
function (Figure 1A). Up to ten solutions were generated for
each metabolite (Supplementary Table S1) of which we chose
two solutions routes (S1 and S2) corresponding to its metabolic
production and consumption route. These were categorized on
how they incorporated the metabolites into RECON 1 (Figure 1B)
and whether they corresponded to biologically plausible gapfilling solutions (Figure 2C) (defined in the Experimental section).
Owing to the reversibility of enzymatic reactions, we did not
assign solution routes specifically as production or consumption
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Figure 2
Ó. Rolfsson and others
Characterization of identified orphan metabolites and the properties of their computed solutions
(A) The distribution of identified metabolites according to their chemical compound superclass. (B) The human biofluids in which the orphan metabolites were detected. (C) Computed candidate
solutions for each metabolite were accepted, categorized as a valid hypotheses or rejected. Solutions were further characterized as type I, type II or type III solutions as outlined in Figure 1(B).
(D) Computed candidate solutions with respect to their metabolite class.
routes. A total of 119 metabolites had candidate solutions
that were either accepted or corresponded to hypotheses. The
remaining metabolites had solutions that were discarded (128) or
had no solution at all (95). This filtered gap-filling output was the
main focus of the following report (Supplementary Table S2).
Accepted candidate solutions
Analysis of the candidate solutions revealed that not all of the
metabolites identified corresponded to orphan metabolites. In
fact, solutions for 73 metabolites could be added directly to
RECON 1 (Figure 2C). In all cases, the reactions associated
with these metabolites, with the exception of added transport
reactions, were either associated with known human genes
or biochemically identified orphan enzyme activities in humans or
closely related mammals. With these metabolites, the gap-filling
algorithm expanded the metabolic reconstruction through addition
of characterized human reactions and metabolites. For example,
cholesterols and derivatives represented the most populous
group of compounds with accepted solutions (Figure 2D). Five
oestrogens were identified that could be added directly to
RECON 1 using type I or II solutions, thereby expanding the C18
steroid biosynthesis pathway of RECON 1 (Supplementary Figure S1 at http://www.biochemj.org/bj/449/bj4490427add.htm). A
total of 58 out of the 73 metabolites with accepted solutions
had type I or II solutions. Having been identified in biofluids,
c The Authors Journal compilation c 2013 Biochemical Society
metabolites with type I solutions suggest that at least a portion of
these can be produced and consumed within the cell.
Metabolites with type III solutions do not add to a
reconstruction knowledgebase in the same manner as type I and
II solutions. Type III solutions introduce flux loops into the
reconstruction. The plant metabolite phytosterol, for example,
was connected to RECON 1 in three reactions: transport into
the cell, conversion by -sterol reductase (EC 1.3.1.72) activity
into isofucosterol and subsequent transport out of the cell.
Contextualization of this type of knowledge is, however, important
in terms of modelling sterol metabolism, where biological
effects of these compounds could be coupled to knowledge of
their metabolic interconversions. Any abnormalities of -sterol
reductase activity could then be associated with biological effects
of these compounds.
Computed candidate solutions direct hypothesis generation
As a next step we analysed how many biologically plausible
hypotheses could be obtained using the gap-filling algorithm by
a thorough literature review. We identified 47 orphan metabolites
with plausible hypotheses. Most of these applied to organic acids,
alcohols, nucleoside derivatives and amino acids (Figure 2D). A
total of 22 of these metabolites could be connected to RECON
1 using type I solutions. One of these was cysteine S-sulfate,
an NMDA (N-methyl-D-aspartate) receptor agonist excreted into
Gap-filling metabolic networks post-reconstruction
Figure 3
431
Orphan metabolites and examples of gap-filling candidate solutions
Cysteine S -sulfate and D-gluconate were incorporated via type I solutions, 5α-androstan-3β,17β-diol via a type II solution and menthol with a type III solution. Solid arrows and metabolites in dark
grey represent metabolic reactions and metabolites within RECON 1 before the present study. Dashed arrows represent reactions added by the gap-filling algorithm to incorporate the human orphan
metabolite (white). EC numbers are indicated in bold. Metabolites in light grey were not in RECON 1 and were not detected as orphan metabolites, but were added in order to incorporate the
orphan metabolite. EC numbers in italic and dotted arrows represent functions identified following a review of the literature.
the urine of individuals with sulfite oxidase deficiency [24].
Cysteine S-sulfate was connected to RECON 1 through the
L-cystine and L-cysteine by the addition of three enzymatic
reactions and the metabolite O-acetyl-L-serine (Figure 3). The
formation of cysteine S-sulfate from L-cystine was deemed
plausible as glutathione-CoA-glutathione transhydrogenase (EC
1.8.4.3) is an orphan reaction in rat and bovine [25] and
has also been shown to be spontaneous and reversible [26].
Conversion of cysteine S-sulfate into L-cysteine involved cysteine
synthase (EC 2.5.1.47), cystathionine δ-synthase (EC 2.5.1.48)
and O-acetylhomoserine aminocarboxypropyl-transferase (EC
2.5.1.49). These three enzymes have sequence similarity to two
PLP (pyrridoxal 5-phosphate)-dependent enzymes of the human
trans-sulfuration pathway, cystathione β-synthase (EC 4.2.1.22)
and cystathionine δ-lyase (EC 4.4.1.1), which have recently been
shown to have broad substrate specificity in the β-replacement
reactions and in the elimination reactions that these enzymes
catalyse respectively [27,28]. Cystathionine β-synthase is, for
example, known to catalyse the α,β-elimination reaction of
L-cysteine to produce L-serine [27,28]. Whether these two
enzymes are capable of eliminating thiosulfide from cysteine
S-sulfate, to generate serine and thiosulfate, has not been
experimentally determined.
Type I solutions were also obtained for compounds originating
from coffee or tea, such as theophyllin, theobromine and caffeine.
The metabolism of these compounds is fairly well characterized,
although the enzymes associated with their degradation have not
been fully characterized and the resulting excreted metabolites
are heterogeneous. Accordingly, the solution output consisted
of multiple degradation routes, none of which could be
discriminated against and highlights a shortcoming of automated
hypothesis generation when multiple solutions are correct or
can be obtained (Supplementary Figure S2 at http://www.
biochemj.org/bj/449/bj4490427add.htm).
Type II solutions were obtained for 18 out of the 47 metabolites.
Among these was 5α-androstane-3β,17β-diol, a steroid that has
been shown to exert an inhibitory effect on prostate cancer
cell migration via activation of oestrogen receptor β [29]. The
candidate solution involved production from dihydrotestosterone
with 3β (or 20α)-hydroxysteroid dehydrogenase (EC 1.1.1.210).
Its activity has been detected in ox and rat but has not been
associated with a gene in mammals [30]. A recombinant 3βhydroxy-5-steroid dehydrogenase (EC 1.1.1.145) was, however,
previously shown to carry out this reaction (Figure 3) [31].
A total of seven metabolites had type III solutions. For
example, the metabolic route of menthol was predicted
to involve intracellular transport, oxidation of menthol to
generate p-menthane-3,8-diol followed by extracellular transport
of p-menthane-3,8-diol (Figure 3). Menthol is known to enter
either enterohepatic circulation as a biliary metabolite or be
oxidized by cytochrome P450 generating menthol diols that are
then excreted into the urine [32,33]. Type III solutions were
also obtained for valproic acid, an epileptic drug. These two
examples highlight the need for a detailed reconstruction of
xenobiotics, which are currently not captured in RECON 1.
Type III solutions were also obtained for methylated nucleosides,
well-known cancer markers, which are also not represented in
RECON 1 as they are derived from the degradation of RNA and
therefore are beyond its scope.
Experimental validation of a computed hypothesis
Candidate reaction solutions remain hypotheses until experimentally proven. We performed experimental confirmations
of the type II solution obtained for gluconate. Gluconate is
the C-1-oxidized linear derivative of glucose and a common
food and drug constituent [34]. The proposed metabolism
involved the oxidation of glucose followed by hydrolysis of Dglucono-1,5-lactone to generate D-gluconate that is subsequently
metabolized upon phosphorylation in the PPP (pentose phosphate
pathway) (Figure 3). Hexose-1-dehydrogenase (EC 1.1.1.47) and
gluconolactonase (EC 3.1.1.17) activity have previously been
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432
Ó. Rolfsson and others
Figure 4 End point enzyme assays of overexpressed C9orf103 in HeLa
lysates confirms gluconokinase activity of isoform I of C9orf103
A 6PGDH-coupled enzyme assay of gluconokinase activity in HeLa lysates overexpressing
isoform I or isoform II of C9orf103 or GFP. More NADPH is generated in the presence of isoform
I as compared with isoform II and the negative control GFP (A). ATP end point measurements in
the gluconokinase reactions show a drop in ATP concentration consistent with ATP-dependent
kinase activity for isoform I (B). A similar effect on NADPH and ATP concentrations was
observed in the presence of the positive control E. coli gluconate kinase (GlcnK_Ecoli). All
reaction mixtures contained ATP at a final concentration of 1 mM and were diluted 10× before
performing the ATP assay.
reported in humans [35,36]. An enzyme with gluconokinase
activity has also been previously purified and characterized from
pig kidney extracts [37]. Performing a homology search of
bacterial enzymes having gluconokinase activity (EC 2.7.1.12),
we identified C9orf103 as a plausible candidate gene, which
may be capable of providing a catabolic route for gluconate
in humans. A BLAST query of isoform I of C9orf103 against
the E. coli genome revealed a 40 % sequence identity to
gluconokinase (gene symbol GNTK) with an E value of 8 − 40
and over 90 % sequence coverage. Furthermore, 12 out of the
14 amino acid residues that have been shown to be important
for E. coli gluconokinase substrate binding are conserved in
C9orf103 (Supplementary Figure S3 at http://www.biochemj.
org/bj/449/bj4490427add.htm).
In order to confirm gluconokinase activity of C9orf103,
we overexpressed isoforms I and II of C9orf103 in human
HeLa cell lysates (Supplementary Figure S4 at http://www.
biochemj.org/bj/449/bj4490427add.htm) and assayed for gluconokinase activity using a 6PGDH-coupled enzyme assay.
Following incubation of cell lysates with added gluconate,
we detected more NADPH in lysates containing overexpressed
isoform I than with isoform II or the negative control containing
GFP. The NADPH concentration was similar to that observed for
the E. coli gluconokinase positive control. Little or no NADPH
was detected in the water negative control (Figure 4A). These
data suggest that C9orf103 encodes an active gluconokinase. The
difference in NADPH concentration can be largely attributed to
the reduction of NADP + that accompanies the oxidation of 6phosphogluconate produced by the C9orf103 gene product. A
c The Authors Journal compilation c 2013 Biochemical Society
drop in ATP concentration upon gluconate addition was also
observed in the lysate expressing isoform I, consistent with ATPdependent kinase activity (Figure 4B). A change in both NADPH
and ATP concentration was also observed upon gluconate addition
in isoform II and GFP samples, perhaps hinting at background
gluconokinase activity in these lysates.
To further confirm the gluconokinase activity of isoform I
and completely rule out activity of isoform II or background
activity, we quantified gluconate and 6-phosphogluconate in the
cell lysates using LC-MS (Figure 5 and Supplementary Figure S5 at http://www.biochemj.org/bj/449/bj4490427add.htm).
Component analysis showed little or no gluconate in isoform I
and in the positive-control samples, whereas 6-phosphogluconate
was readily detected. The opposite was true for isoform II and
the negative-control samples. An experiment where uniformly
labelled [13 C]gluconate was used as the substrate unequivocally
demonstrates that the added gluconate is indeed converted into
6-phosphogluconate in the presence of overexpressed isoform I.
No kinase activity was observed for isoform II above the negative
control. This is most likely due to the absence of the phosphatebinding loop binding domain that is present in the N-terminal
domain of isoform I but missing in isoform II (Supplementary
Figure S3), suggesting a different function for this isoform. These
data confirm gluconokinase activity in humans and support a
gluconate degradation route through the PPP similar to the one
observed in lower organisms [38].
Metabolites with discarded or no solutions
A large proportion of the metabolites identified had either
candidate solutions that were discarded or had no solutions
at all (Figure 2C). Approximately 60 % of the metabolites
having discarded solutions were organic acids, alcohols or amino
acid derivatives, which also represented the most numerous
metabolite classes. In the majority of the cases, no evidence
was found to support the occurrence of the proposed solutions in
humans. Some metabolites were also found to not correspond to
orphan metabolites. Exogenous compounds, such as rosmarinic
acid and curcumin, have been shown to be excreted following
glucuronidation or glycosylation [39,40]. Similarly, compounds,
such as secondary bile acids, tartarate, raffinose, melbiose and
6-hydroxynicotinic acid, which are known to be indirectly
metabolized in humans by the gut microbiome, do not need to
be accounted for in RECON 1. Compounds that had no solutions
were mostly inorganic compounds, amino acid derivatives,
organic acids (Figure 2D) and also compounds which are not
associated with basal metabolism, such as arsenic, lead, ibuprofen
and naproxen.
DISCUSSION
In the present study, RECON 1 was used as a discovery tool
alongside a library of chemical reactions to computationally
propose metabolic routes for human orphan metabolites found
in biofluid samples. The key results include (i) the identification
and characterization of 343 metabolites, (ii) metabolic hypotheses
for multiple orphan metabolites and the biochemically detailed
description of five of these, (iii) the experimental validation of
one of the computationally proposed hypotheses leading to the
characterization of C9orf103 as a human gluconokinase, and
(iv) a large proportion of the identified metabolites could not
be accounted for using our approach and may focus future gapfilling efforts. The results of the present study demonstrate how
gap filling can focus the expansion of metabolic networks and
Gap-filling metabolic networks post-reconstruction
Figure 5
433
LC-MS analysis of gluconate and 6-phosphogluconate composition in HeLa lysates expressing C9orf103
Overlapped extracted ion chromatograms of gluconate (Glcn) and 6-phosphogluconate (6P-Glcn) from gluconokinase assays containing overexpressed C9orf103 isoform I (A and C) or isoform II
(B) in the presence of 1 mM Glcn (A and B) or 1 mM [13 C6 ]gluconate (C). Quantification of the reaction components using ISs showed that gluconate was consumed and 6-phosphogluconate was
produced only in the presence of overexpressed isoform I and of the positive control. Similarly, when [13 C6 ]gluconate was used as the substrate, [13 C6 ]6-phosphogluconate was only detected in
lysates overexpressing isoform I and the positive control (D and E).
facilitate metabolite and gene annotation through a metabolitecentred approach.
Our approach assumed that RECON 1 correctly captured
current knowledge and, therefore, that all metabolites identified
were orphan metabolites. Clearly this was not the case as more
than one-fifth of the metabolites could be added directly to
RECON 1, thus not being true orphan metabolites. This result
demonstrates that the metabolic network reconstruction process is
iterative requiring periodical refinement so that the network model
sustains its biological accuracy. With respect to the accepted
solutions, the gap-filling algorithm therefore fulfils its role as
a network gap-filling tool [9].
We generated biologically plausible hypotheses for almost 50
orphan metabolites. The notion that almost half of these have
proposed anabolic and catabolic routes infers that their detection
in biofluids may be associated with abnormal metabolic states,
as is the case for cysteine S-sulfate (OMIMM# 272300). The
computed hypotheses (Figure 3) conforms with the previously
proposed metabolism of cysteine S-sulfate from L-cysteine in
the pathogenesis of sulfite oxidase deficiency [38] albeit in biochemical detail. The second computed solution, through
L-serine, is also plausible on the account of the catalytic
promiscuity of the PLP-dependent enzymes involved towards
sulfur-containing amino acids [27,28]. PLP-dependent enzymes
have broad catalytic activity on account of the diverse reactivity
of the PLP Schiff base intermediate [41]. For modelling of
disease-associated metabolic phenotypes, such as inborn errors
of metabolism [42], these types of discoveries are valuable. The
remainder of the hypotheses involved type II and III solutions.
Interestingly, during the preparation of the present paper the
computed candidate reaction for menthol was confirmed to be
catalysed by CYP2A6 in human liver microsomes by Nakanishi
and co-workers [43]. Multiple approaches have been devised to
derive gene or protein function [44]. With the recent surge in non-
targeted metabolite profiling, the number of metabolites whose
biological roles are not defined will surely increase [45]. The three
solution types highlight how network gap filling directs metabolic
hypotheses of orphan metabolites through contextualization of
biological data. The linkage between metabolites and genes can
therefore be successfully done in a semi-automated manner using
a high-quality genome-scale metabolic reconstruction.
Our network gap-filling approach suffers a drawback in that
it is not fully automated. Since the universal reaction database
(KEGG) used captures biochemical reactions identified in all
domains of life, manual ranking and curation of candidate
solutions was required before addition of computed reaction solutions to the network, similar to the laborious bottomup network reconstruction procedure [46]. The prioritization of
predicted pathways between metabolites has been a focus point
in synthetic pathway engineering, where candidate reactions
between metabolites have been ranked according thermodynamic
feasibility, pathway length, chemical similarity and organism
specificity [14]. Automated ranking and filtering of the gap-filling
output would be valuable when faced with multiple candidate
solutions.
We confirmed the computationally proposed hypotheses for
gluconate consumption and showed that C9orf103 encodes a
functional gluconokinase. The proposed metabolism of gluconate
is identical with what is observed in lower organisms where
it has been extensively characterized [38]. The molecular
details of gluconate metabolism in mammals are less well
understood, although early biochemical experiments suggest a
similar degradation route. Using 14 C-labelled gluconate, Stetten
and Topper [47] demonstrated that the carboxyl carbon of
gluconate yields CO2 abundantly in intact rats. Because reduction
of gluconate directly to glucose had been shown to not take
place [47], the finding was consistent with omission of CO2
from 6-phosphogluconate by 6PDGH (EC 1.1.1.44) [47,49].
c The Authors Journal compilation c 2013 Biochemical Society
434
Ó. Rolfsson and others
C-6 of gluconate was, however, observed to contribute to fatty
acid synthesis, presumably following integration into glycolysis
through the non-oxidative branch of the PPP [50]. These studies
of gluconate metabolism took part in pioneering investigations
into oxidative glucose metabolism that aided in the discovery of
the PPP [51]. An active gluconokinase was later characterized and
purified from rat and pig kidney [37].
We have demonstrated for the first time that gluconokinase
activity is encoded within the human genome. C9orf103 was
initially identified during cDNA library construction [52] and
later shown to be located within the commonly deleted region of
del(9q) acute myeloid leukaemia [53]. These studies did not report
on the possible function of the gene. C9orf103 was, however,
electronically annotated prior to the present study as a putative
gluconokinase. A similarity in protein sequence does not always
transfer to enzyme function [54]. Isoform II was, for example,
shown in the present study to be catalytically inactive. Also, an
18 amino acid insertion within both isoforms of C9orf103 as
compared with E. coli GlcnK (Supplementary Figure S3) does
not appear to abolish the gluconokinase activity of isoform I.
The location of this peptide insertion within the amino acid
sequence of isoform I hints at a structural difference between
isoform I and E. coli GntK previously observed between E.coli
GlcnK and various NMP kinases that are known to catalyse the
phosphorylation of a broad range of substrates [55]. Although
this might suggest additional functions for the C9orf103 protein
product, the gluconokinase purified from pig kidney was found
to exhibit strict specificity for D-gluconate [37]. Ultimately, the
result of the present study provides a missing genetic link between
mammalian gluconokinase activity and our computationally
proposed gluconate catabolic route previously proposed through
biochemical pulse–chase experiments in rats.
An anabolic route for gluconate through the oxidation of
glucose was also computed. Although both activities for this
conversion are found in humans, cell biology experiments will be
required to confirm whether these act synergistically to generate
gluconate in vivo. There is no apparent gain of oxidation of glucose
through gluconate compared with glucose 6-phosphate. Both
presumably yield the same pentose phosphates and two molecules
of NAPDH. This result does, however, not rule out a metabolic
contribution of gluconate as a nutrient with a catabolic role via the
PPP following, for example, cellular uptake or as a degradation
product of intracellular carbohydrates. Similar experiments would
also shed light on the effect of down-regulation of C9orf103
associated with del(9q) acute myeloid leukaemia [53].
We identified more than 200 orphan metabolites for which
no solution could be obtained as their metabolism was beyond
the scope of RECON 1. These metabolites point to reaction
pathways that should be incorporated into RECON 1 to account
for metabolic phenotypes associated with, for example, phase I
and II xenobiotic detoxification routes, the contribution of the
human microbiome, signalling cascades, and transcription and
translation. Integration of such non-metabolic reconstructions
would help address orphan metabolites, whose metabolic fate lies
beyond the scope of RECON 1 and subsequently better define a
list of true human orphan metabolites.
The finding that RECON 1 can be used to generate
computational hypotheses of human biological functions is
intriguing as it implies that metabolic models can be used to
prioritize the discovery of human metabolic functions albeit by
focusing on metabolites rather than genes. This prioritization
stems from filling gaps in knowledge of human metabolism,
thereby systematically expanding metabolic knowledge through
a bottom-up approach. This approach does, however, not unearth
functions that are globally uncharacterized as the method relies on
c The Authors Journal compilation c 2013 Biochemical Society
annotated functions and genes. The manner in which incomplete
biological data is put into context with current metabolic
knowledge is therefore the driving force behind this approach
of biological target identification and subsequent functional
validation. The result is an expanded metabolic reconstruction,
which better captures and defines the known and unknowns of
human metabolism.
AUTHOR CONTRIBUTION
Óttar Rolfsson designed the study, performed the experiments, analysed the data and wrote
the paper. Giuseppe Paglia and Manuela Magnusdóttir performed MS analysis. Bernhard
Palsson contributed to experimental design and writing of the paper. Ines Thiele designed
the study, performed the experiments and contributed to the writing of the paper.
FUNDING
This work was supported by the European Research Council [grant number 232816].
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Received 18 June 2012/13 September 2012; accepted 15 October 2012
Published as BJ Immediate Publication 15 October 2012, doi:10.1042/BJ20120980
c The Authors Journal compilation c 2013 Biochemical Society
Biochem. J. (2013) 449, 427–435 (Printed in Great Britain)
doi:10.1042/BJ20120980
SUPPLEMENTARY ONLINE DATA
Inferring the metabolism of human orphan metabolites from their metabolic
network context affirms human gluconokinase activity
Óttar ROLFSSON*†, Giuseppe PAGLIA*, Manuela MAGNUSDÓTTIR*, Bernhard Ø. PALSSON* and Ines THIELE*‡1
*Center for Systems Biology, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland, †University of Iceland Biomedical Center, Laeknagardur, 101 Reykjavik, Iceland, and
‡Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland,101 Reykjavik, Iceland
Figure S1
algorithm
Four metabolites derived from oestrone that have been identified in human biofluids and that were incorporated into RECON 1 using the gap-filling
Selected reactions and metabolites in C18 steroid metabolism, as they appear in a decompartmentalized model of RECON 1, are shown in blue. The gap-filling solutions for 2-methoxy-estradiol-17β,
2-methoxyestrone, 2-hydroxyoestrone and 16-α-hydroxyoestrone are shown with green arrows. 16-α-Hydroxyoestrone has a type I solution, whereas the remainder have type II solutions. Note that
the type II solution was also a gap-filling solution in RECON 1 in that it connected oestriol to the network, which would otherwise be caught in a loop and did not carry flux.
1
To whom correspondence should be addressed (email [email protected]).
c The Authors Journal compilation c 2013 Biochemical Society
O. Rolfsson and others
Figure S2
Reconstruction of caffeine and theobromine metabolism
Manual curation is required in order to generate biologically plausible metabolic pathways for highly interconnected metabolites. An example of where automated gap filling and hypotheses
generation fell short. The primary metabolic degradation routes of caffeine, theobromine and its derivatives were through xanthine, which was then excreted as uric acid, and through the excretion
of 5-acetylamino-6-amino-3-methyluric acid. The gap-filling algorithm is capable of proposing multiple degradation routes, the manual curation of which are required in order to accurately reflect
caffeine metabolism.
c The Authors Journal compilation c 2013 Biochemical Society
Gap-filling metabolic networks post-reconstruction
Figure S3
Sequence alignment of isoform I and II of C9orf103 to the amino acid sequences of the gluconokinases of E. coli
Gluconokinase is encoded in E. coli by two genes, which encode a thermoresistant (GNTK ) and thermosensitive (IdnK ) gluconokinase. C9orf103 has 40 % and 39 % sequence identity to these
genes respectively (BLAST data not shown). Residues that have been shown to be important for gluconic acid binding in E. coli GNTK [1] are highlighted in red. The phosphate-binding domain
P-loop is highlighted in green and is absent from C9orf103 isoform II. Colouring is according to percentage identity. The alignment and Figure were generated with Jalview.
c The Authors Journal compilation c 2013 Biochemical Society
O. Rolfsson and others
Figure S4 Western blots of coupled in vitro transcription/ translation reactions of C9orf103 isoform I (pT7CFE1-GlncKIsoI) (A) and C9orf103 isoform II
(pT7CFE1-GlncKIsoII) (B) using 0.5–2.0 μg of linearized plasmid template (lanes 1–8)
GFP template DNA (0.5 μg) was used in positive-control reactions. Water was used as a negative control. In total 1 μl and 3 μl from each 25 μl reaction mixture were loaded and run on 10 % Bis/Tris
NuPAGE gels for 50 min. Using a supersignal West HisProbe Kit, both isoforms could be detected irrespective of DNA template amount used following transfer on to a nitrocellulose membrane. These
are highlighted with red arrows. A BenchMark His-tagged Protein Standard (Invitrogen) was used to assess molecular mass using Image Lab software (Bio-Rad Laboratories). C9orf103 isoform I
+
was observed to have a molecular mass of 22.4 +
− 0.17 kDa (calculated molecular mass 22.328 kDa). C9orf103 isoform II was observed to have a molecular mass of 29.1 − 0.22 kDa (calculated
molecular mass 27.047 kDa).
c The Authors Journal compilation c 2013 Biochemical Society
Gap-filling metabolic networks post-reconstruction
Figure S5
MS/MS (tandem MS) spectra of gluconate and 6-phosphogluconate
(A) MS/MS spectrum of 6-phosphogluconate during ISO I assay. (B) MS/MS spectrum of [13 C6 ]6-phosphogluconate during ISO I* assay using [13 C6 ]gluconate.
REFERENCE
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catalytic cycle of gluconate kinase as revealed by X-ray crystallography. J. Mol. Biol. 318,
1057–1069
Received 18 June 2012/13 September 2012; accepted 15 October 2012
Published as BJ Immediate Publication 15 October 2012, doi:10.1042/BJ20120980
c The Authors Journal compilation c 2013 Biochemical Society